The Investigation of EDM Parameters on Electrode Wear Ratio

Transcription

1 Research Journal of Applied Sciences, Engineering and Technology 4(10): , 2012 ISSN: Maxwell Scientific Organization, 2012 Submitted: December 09, 2011 Accepted: January 04, 2012 Published: May 15, 2012 The Investigation of EDM Parameters on Electrode Wear Ratio Reza Atefi, Navid Javam, Ali Razmavar and Farhad Teimoori Department of Mechanical Engineering, Dehaghan Branch, Islamic Azad University, Dehaghan, Isfahan, Iran Abstract: Electrical Discharge Machining (EDM) is a well-established machining option for manufacturing geometrically complex or hard material parts that are extremely difficult-to-machine by conventional machining processes. The non-contact machining technique has been continuously evolving from a mere tool and die making process. In this study, the influence of different electro discharge machining parameters (current, pulse on-time, pulse off-time, arc voltage) on the electrode wear ratio as a result of application copper electrode to hot work steel DIN has been investigated. Design of the experiment was chosen as full factorial. Artificial neural network has been used to choose proper machining parameters and to reach certain electrode wear ratio. Finally a hybrid model has been designed to reduce the artificial neural network errors. The experiment results indicated a good performance of proposed method in optimization of such a complex and non-linear problems. Key words: Artificial Neural Network (ANN), Electrical Discharge Machining (EDM), electrode wear ratio, hybrid model INTRODUTION The origin of Electro Discharge Machining (EDM) dates back to 1770 when an English scientist Joseph Priestly discovered the erosive effect of electrical discharges. Pioneering work on electrical discharge machining was carried out in 1943 during World War II by two Russian scientists (B.R. and N.I. Lazarenko) at the Moscow University (Kumar et al., 2009). Shaping of materials for modern manufacturing industries with stringent design requirements, such as high precision, complex shapes, and high surface quality, is inevitable to put them in use (Jain and Dixit, 2004). To achieve these objectives, advanced machining processes are required.moreover, advanced machining techniques have classified into four types (Jain, 2001). Mechanical, thermal, chemical machining and electrochemical machining, and biochemical machining processes. Among these, EDM is a thermal process which has been widely used to produce dies and molds (Abbas et al., 2007). This high technology is developed in the late 1943s, which supports about 7% of all machine tool sales in the world (Moser, 2001). Its unique feature of using thermal energy to machine electrically conductive parts regardless of hardness has been its distinctive advantage in the manufacture of mold, die, automotive, aerospace and surgical components (Ho and Newman, 2003). Lee and Li (2001) analyzed the effect of machining parameters on the machining characteristics EDM of tungsten carbide. They study the influence of different EDM parameters on the surface roughness, Material Removal Rate (MRR) and electrode wear ratio (EWR) (Lee and Li, 2001). Puertas et al. (2004) analyzed the effective parameters on surface roughness; MRR and EWR in EDM. They evaluate the effect of current, pulse on-time and pulse off-time on surface roughness, MRR and EWR on finishing stage. They present proper second degree regression models for predicting surface roughness, material removal rate and electrode wear (Puertas et al., 2004). Salman and Kayacan (2008) evaluated the effect of EDM parameters on surface roughness, volumetric material removal rate and electrode wear. They developed a mathematical model which based on that they could predict surface roughness, MRR and electrode wear by changing the pulse on-time, current and pulse voltage (Salman and Kayacan, 2008). In this study, the influence of different EDM parameters (current, pulse on-time, pulse off-time, arc voltage) on the EWR as a result of application copper electrode to hot work steel DIN has been investigated. Design of the experiment was chosen as full factorial. Artificial neural network has been used to choose proper machining parameters and to reach certain EWR. Finally a hybrid model has been designed to reduce the artificial neural network errors. The experiment results indicated a good performance of proposed method in optimization of such a complex and non-linear problems. Procedure: In this section, there will be a brief description of the equipment and material used to carry out the EDM experiments. Also, the design factors used in this study will be outlined. orresponding Author: Reza Atefi, Department of Mechanical Engineering, Dehaghan branch, Islamic Azad University, Dehaghan, Isfahan, Iran. 1295

2 Table 1: Details of work piece, tool and dielectric fluid Electrode Workpiece Dielectric fluid opper (electrolytic grade) Hot Work Steel: (Kerosene) DIN Dimension: cylindrical shape omposition-: with a diameter of 10 mm 0.39%; r:5.15%; (10 mm 10 mm 25 mm) Mo: 1.25%; V: 1%; Si: 1%; Mn: 1%; rest iron D e (density of copper) is Dimension: cylindrical 8.9/10 3 g/mm 3 shapewith a diameter of 25 mm( 25 mm 25 mm 5 mm) D w (density of steel) is 7.8/10 3 g/mm 3 Table 2: Experimental machining setting urrent Gap voltage Pulse on- Pulse off- Electrode Jet (I) (V) time (t on ) time (t off ) polarity flushing 4, 6, 40, 60, 25, 50, 25, 50, Positive pressure 8A 80 v 100 :s 100 :s (+) 25 kpa Equipment used in the experiment: Die-sinking EDM machine: Die-sinking EDM machine used in this experiment was Roboform 40 manufactured by harmilles Technologies Machine. It has 4 axial movements (linear movement in X, Y and Z axis and rotational movement in Z axis). Movement resolution of EDM machine was 0.5 microns. Digital weighing machine: Digital weighing machine (used for checking the weight of samples) was model 100 manufactured by GB o., USA (precision of 0.01 g). MATERIALS AND METHODS A new set of instrument (electrode and workpiece) for each experiment has been used. The machining state has been shown in Table 1. The purpose of doing the experiment was the electrode wear ratio in EDM finishing stage of hot work steel DIN and presenting an appropriate ANN for the prediction of EWR. As the aim of experiment was evaluation of EWR in finishing stage, the work pieces have been selected to be drilled 0.2mm deep in the surface. The most important parameters in EDM are pulse current (I), pulse voltage (V), pulse on-time (T on ) and pulse off-time (T off ) (Lee and Li, 2001; Salman and Kayacan, 2008). This study employed a full factorial design because ANN model needed a lot of data to obtain an appropriate model for EWR prediction. Pulse current 3 to 8 Ampere was selected for EDM finishing and as a result, pulse currents 4, 6, 8A were used. Pulse voltages 40, 60, 80 v were used based on available pulse voltages EDM machine. Pulse on-times 25, 50, 100 :s were used. The pulse-off duration is equal to the pulse-on therefore pulse off-times 25, 50, 100 :s were used. Therefore, in this study, 81 experiments were done on Work pieces. The Experimental machining setting has been shown in Table 2. Parameters explained above used as experimental variables and it defined the value of EWR. MRR and EWR can be calculated by the following equations (Habib, 2009): MRR Ww 3 = ρ w t (min / min) VEW = We ( mm 3 /min) ρ et 100 VEW EWR = MRR (1) (2) (3) VEW is the volumetric electrode wear, W w is the workpiece weight loss in g, W e is the electrode weight loss in g, T is the machining time in min, D s is the density of workpiece(steel) and D u is the density of electrode (copper). RESULTS AND DISUSSION All of the 81 electrode wear ratio values measured as a result of the EDM based on parameters such as the discharge current, pulse on-time, pulse off-time and gap voltage have been indicated in Table 3. Design of the artificial neural network model: Artificial Neural Network (ANN) has been designed for the prediction of EWR. For designing and training of ANN model, the programming in Matlab software was used. Training procedures were as follow: Defining the inputs and outputs of the network Defining error function of the network Obtaining the trained output data for input vector data omparing real outputs with test outputs orrecting ANN weights based on error value Repeating "orrect ANN weights based on error value" to reach minimum error The input parameters considered in the experiments include discharge current (I), voltage (V), pulse-on time (T on ) and pulse-off time (T off ). The output parameter considered in experiments includes surface roughness (R a ). Architecture of ANN model is shown in Fig. 1. Error function network used mean square error (MSE) procedure as shown in the following equation (Mandal et al., 2007): N m 2 1 MSE = Tj Oj N ( ) 2 i= 1 j= 1 (4) 1296

3 Table 3: Resault of the EDM experiment EWR T on T off T on T off EWR T on T off EWR No I(A) V(v) (%) (:s) (:s) No I(A) V(v) (:s) (:s) (%) No I(A) V(v) (:s) (:s) (%) prediction vale% = (actual value - predicted value/ actual value) 100 (5) Fig. 1: Architecture of ANN model N is the all number of training pattern (definition of epoch in Matlab programming), m is the number of output nodes, T j is the target output of the jth neuron and O j the estimated value of the jth neuron (Puertas et al., 2004). Designing the ANN model for EWR value estimation: The number of data is 81.consequently, 9 out of 81 were chosen for testing of the network and 72 for training the network. The number of neurons was selected in hidden layers, transportation function of each neuron, error training method based on minimum error. For testing the prediction ability of the prediction error model in each output, node has been calculated as follows (Mandal et al., 2007): In this situation Mean prediction error has a minimum value and network architecture is in the best situation. The choose of the number of neurons in hidden layers, transportation function of each neuron, learning method and training method was based on trial and error to obtain minimum error. The designed ANN had 4 inputs, 31 neurons in first hidden layer, 31 neurons in second hidden layer and 1 neuron in output layer. The training of network used trainrp (back propagation) method. 0.3 is used as the value of MSE. The maximum, minimum and mean prediction error with different architectures network for selection neurons has been shown in Table 4. For the reduction of ANN errors and precise estimation of EWR, a hybrid model was used (a combination of statistical method and neural network(. For this reason, by doing a statistical analysis, values removed with high residuals in Table 3 (NO.6, 13, 26, 39). We have the value of 77 EWR which 68 values were used for network training and 9 values for network test. The designed ANN had 4 inputs, 20 neurons in first hidden layer, 21 neurons in second hidden layer and 1 neuron in output layer. The maximum, minimum and mean prediction errors for this network are 6, 0.05 and 2%, respectively. Mean prediction error has been calculated by taking the average of all the individual errors, for all the testing patterns. The maximum, 1297

4 Table 4: Different architectures network for ANN model Network Minimum Maximum Mean prediction Serial no architecture prediction error (%) prediction error (%) error (%) Table 5: Different architectures network for hybrid model Network Minimum Maximum Mean Serial no architecture prediction error (%) prediction error (%) prediction error (%) minimum and mean prediction error with different architectures network for selection neurons has been shown in Table 5. Using hybrid model caused mean error reach to 2% which showed 1.1% less error in compared to the experiments that ANN was used. The results show good performance of proposed model when we optimize such a complex and non-linear problems. ONLUSION In this study, the influence of different EDM parameters (current, pulse on-time, pulse off-time, pulse voltage) in finishing stage on the surface quality (R a ) as a result of application copper electrode to a work piece (hot work steel DIN1.2344) has been investigated. ANN has been designed for the prediction of EWR in finishing stage of hot work steel DIN Finally for reducing the error in ANN, a hybrid model (a combination of statistical analysis and ANN model) has been designed and following results has been obtained: Application of ANN to predict EWR is a scientific method which makes industries free from complex traditional trial and error methods. By using ANN and correct training of it, without doing any test, we can precisely predict EWR by changing current, pulse on-time, pulse off-time and arc voltage. Designed ANN has mean error of 3.1% and maximum error of 8%. By using a hybrid model, mean error of ANN had reduced to 1.1% and reached to 2%. The results show good performance of proposed method in optimization of complex and non-linear problems. This error level shows a good precision for EWR. REFERENES Abbas, N.M., D.G. Solomon and M.F. Bahari, A review on current research trends in Electrical Discharge Machining (EDM). Int. J. Mach. Tool. Manuf., 47: Habib, S., Study of the parameters in electrical discharge machining through Response surface methodology approach. Appl. Math.l Mode., 33: Ho, K.H. and S.T. Newman, State of the art Electrical Discharge Machining (EDM). Int. J. Mach. Tool. Manuf., 43: Jain, V.K., Advanced Machining Processes. Allied Publisher, Bombay. Jain, P.M. and Dixit, Parametric study of temperature distribution in electrodischarge diamond grinding. Mater. Manuf. Process., 19: Kumar, S., R. Singh, T.P. Singh and B.L. Sethi, Surface modification by electrical discharge machining: A review. J. Mater. Proc. Technol., 209(8): Lee, S.H. and X.P. Li, Study of the effect of machining parameters on the machining characteristics electrical discharge machining of tungsten carbide. J. Mater. Proc. Technol., 115:

5 Mandal, D., S.K. Pal and P. Saha, Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-ii. J. Mater. Proc. Technol., 186: Moser, H., Growth industries rely on EDM. Manuf. Eng., 127: Puertas, I.,.J. Luis and L. Álvarez, Analysis of the influence of EDM parameters on surface quality, MRR and EW of W-o. J. Mater. Proc. Technol., 154: Salman, O.Z. and M.. Kayacan, Evolutionary programming method for modeling the EDM parameters for roughness. J. Mater. Proc. Technol., 200: